AnzoGraph® DB

Build Your Solutions on a Fast, Scalable Database

Take on new data harmonization and analytical challenges with AnzoGraph DB, a market-leading graph analytics database

Like all databases, graph databases store facts, but they also keep track of how those facts are connected.

AnzoGraph DB’s flexible data model and analytical capabilities not only let you load diverse data sets but can perform data warehouse-style analytics, graph algorithms, inferencing and more. It’s all about a broad set of analytics on a wide range of data, delivered at unparalleled speed and scale.

Unify data at any scale with industry-standard languages

Use industry-standard language with labeled property graphs to build graph models. The models handle structured and unstructured data from diverse data sources with less worry about tables, JOINs and data lifecycle.

Analyze the relationships

Take on analytical challenges that were difficult or impossible with a traditional RDBMS. Friend-of-a-friend features and graph algorithms get you there faster. Feature engineering algorithms and an SDK let you open the door to machine learning.

Comparing Triplestores

Use Cases and Applications

Knowledge Graph

Organizations are using graph databases to build Knowledge Graphs to provide common business understanding to the data harmonized from diverse sources. Knowledge Graphs stores entities and relationships in data and allows users to search, analyze and use this connected data to accelerate vital new discoveries.

Unstructured Data Analytics

Combined with Natural Language Processing (NLP), graph database offers a free-form repository to store the output of NLP, which is often formatted in RDF triples and use of such data for data discovery and analytics.

Key Influencer Analytics

Analyze all customer data to find key opinion leaders. Gain new insight into each customer’s likes and dislikes in relation to other customers with similar location, similar demographics, etc. Discover new correlations between customers with inferencing, for more personalized and engaging customer experiences.

Recommendation Engine Analytics

Recommendation engines are perfect in a graph database when you want to make use of algorithms and data to recommend the most relevant items to a particular user.

Fraud Analytics

Use Graph to help detect fraudulent trading patterns and transactions in real-time. Semantically identify and understand the intricate relationships between entities and transactions, including the many individuals and organizations involved with those transactions.

Path Optimization Analytics

Analyzing how things (objects) connect and interact with each other can be very powerful. Graph databases are uniquely qualified to help with this relationship analytics.

Social Analytics

One of the original use cases for graph databases is for keeping track of social networks and understanding influence.

AI & Machine Learning

We think that the emerging world of AI and machine learning offer workloads that are well-suited for graph databases. Many of the machine-based algorithms are graph algorithms such as community detection algorithms, pathfinding algorithms, similarity or centrality algorithms.

AnzoGraph DB for Software Developers

On-demand Webinar: Scalable, Fast Analytics with Graph - Why and How

Watch this on-demand webinar as they demonstrate how AnzoGraph DB can be used to do difficult-to-perform analytics on large data sets and to explore and uncover new opportunities using the Graphileon user interface.

Watch this on-demand webinar to explore how portfolio managers are using the Parabole/ AnzoGraph DB integration for conducting ML and cognitive analytics at scale to identify potential risks and new opportunities.